Advanced Deep Learning Approaches for Satellite Image Classification: Trends, Techniques, and Challenges
| International Journal of Electrical and Electronics Engineering |
| © 2025 by SSRG - IJEEE Journal |
| Volume 12 Issue 12 |
| Year of Publication : 2025 |
| Authors : Kinjal Patel, Kaushika Patel |
10.14445/23488379/IJEEE-V12I12P109
How to Cite?
Kinjal Patel, Kaushika Patel, "Advanced Deep Learning Approaches for Satellite Image Classification: Trends, Techniques, and Challenges," SSRG International Journal of Electrical and Electronics Engineering, vol. 12, no. 12, pp. 117-129, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I12P109
Abstract:
For the purpose of detecting satellite images, researchers contributed a significant amount of effort and time over the course of many years in developing a broad number of different techniques. The vast majority of these models are intended to perform a certain task, with a specific kind of satellite image being the primary focus of their efforts. The classification accuracy of complex images continues to be inadequate, despite the fact that there have been developments in satellite image tools and object-based image analysis tools for the purpose of analyzing high-resolution temporal and spatial satellite images. It is possible that the primary reason for this inadequacy is due to the substantial variation in the spectral and spatial properties of the images, which makes the classification of diverse land cover classes more challenging. This paper explores recent advancements in satellite image classification, highlighting deep learning methodologies, multispectral and hyperspectral image analysis, and AI-driven strategies that combine machine learning with remote sensing. It also analyzes the function of transfer learning in satellite image classification, discussing significant challenges and opportunities. A comparative analysis of the available literature is presented, together with insights into existing challenges and future directions.
Keywords:
Deep learning, Land cover classification, Remote sensing, Satellite image classification, Transfer learning.
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